# Artificial Intelligence and Machine Learning in Energy Conversion and Management

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## Abstract

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## 1. Introduction

_{2}emissions goal requires faster progress in implementing new technologies for energy conversion, distribution, and use [5,6,7,8,9,10,11]. There is also a need to accelerate the progress in the hydrogen economy to ensure rapid technology adoption across countries [7].

- Are AI techniques being applied in energy conversion and management fields? If so, which algorithms and for what tasks (Section 3.1 and Section 3.2)?
- What kind of data and data size are researchers in this field using? Are they relying on simulated data or using real-life data to train and test their AI models? Are papers doing a good job at reporting their data? What tools are they using to conduct these studies? What are the data and algorithms’ memory requirements (Section 3.3, Section 3.4 and Section 3.5)?
- What are the trending AI algorithms? How effective or accurate are they? What are their strengths and limitations (Section 4)?

## 2. State of the Art

#### 2.1. Bibliographic Study

- Wind Energy: backpropagation neural networks were effective for wind farm operational planning;
- Solar Energy: modeling and controlling photovoltaic systems using backpropagation neural networks;
- Geothermal Energy: artificial neural networks are key tools for geothermal well drilling plans, their control, and optimization;
- Hydroenergy: hydropower plant design and control implement fuzzy, ANN, adaptive neuro-fuzzy inference system (ANFIS), and genetic algorithms for optimization;
- Ocean Energy: ocean engineering and forecasting rely heavily on ANFIS, back propagation neural networks, and autoregressive moving average models;
- Bioenergy: categorization of biodiesel fuel using KNN, SVM, and similar classification algorithms, as well as a hybrid system, combining the elements of a fuzzy logic and ANN, is employed to enhance the heat transfer efficiency and cleaning processes of a biofuel boiler;
- Hybrid Renewable Energy: ANFIS is utilized in hybrid AI techniques to enhance the performance of hybrid photovoltaic–wind–battery systems and ultimately reduce production costs, as well for the modeling of biodiesel systems, solar radiation, and wind power analysis and wavelet decomposition; ANNs and autoregressive methods are used in solar radiation analysis; SVR+ARIMA ( autoregressive integrated moving average) for the tidal ongoing analysis; improved and hybrid ANNs in photovoltaic system load analysis; and data-mining method-based systematic energy control system.

#### 2.2. Document Information Extraction

- Algorithm: the highlighted algorithm used in the publication;
- Energy Topic: the energy conversion domain the publication relates to (i.e., different kinds of renewable energy, nonrenewable energy, energy conversion systems, etc.);
- Energy Task: the specific task highlighted within an energy-related topic (i.e., forecasting, optimization, etc.);
- Energy Domain: the primary energy resource the publication relates to (i.e., wind, solar, natural gas, etc.);
- Data Size: the number of total observations in the dataset used in the publication;
- Data Type: the origin and nature of the data used (i.e., real data (panel data), simulated data (data created through mathematical and computer simulators), time series (data with dates and/or time), images, etc.);
- Performance Measure: the score of accuracy, error, or percentage improvement achieved by the algorithm;
- Performance Measure Type: the name of the performance measure (i.e., root-mean-square error (RMSE), mean squared error (MSE), mean absolute error, R-squared, etc.);
- Comparative Benchmark Performance: the percentage improvement achieved by the highlighted algorithm either over the traditional non-AI method or the next-best AI method;
- Tools Used: the programming language or software applied to achieve the reported results (i.e., MATLAB, Python, etc.);
- Device Memory: the number of GB in the RAM (random-access memory) of the device used to produce numerical data in the publications.

#### 2.3. Overview of ML and AI Algorithms

#### 2.3.1. AI: Beginning, Winter, and Revival

#### 2.3.2. Supervised and Unsupervised Learning Algorithms

- Supervised learning produces a function that maps inputs to estimated outputs by providing the algorithm input–output pairs during training. Supervised learning algorithms are also mainly used for regression problems [36]. These include, but are not limited to, decision trees, random forest, linear and logistic regression, support vector machine, ANN, and other neural network algorithms that use a labeled training set.
- Unsupervised learning algorithms analyze a dataset comprising solely inputs and discern data structures by examining shared characteristics among data points. Unsupervised learning algorithms are commonly used for grouping or clustering data points. Summarizing and explaining data features are other tasks for these algorithms [36], which include, but are not limited to, k-means clustering, principal component analysis, and hierarchical clustering. There also exists semi-unsupervised learning, where only a small part of the training dataset is labeled.

#### 2.3.3. Reinforcement Learning Algorithms

#### 2.3.4. Other Types of Algorithms

## 3. Machine Learning and Artificial Intelligence in Research on Energy Conversion

#### 3.1. Most Researched Topics, Tasks, and Domains

#### 3.2. Most Popular Algorithms

#### 3.3. Simulated Data vs. Real Data

#### 3.4. Tools

#### 3.5. Memory Requirements

#### 3.6. Research Hotspots and Direction

- Wind Energy Conversion Systems (#0, #4). These two clusters were merged in the analysis as they both describe wind energy conversion. The highest frequency keywords include wind power (141 sources), controller (64 sources), wind turbine (57 sources), maximum power point tracker (41 sources), and neural network (284 sources). In 2016, using MATLAB simulations, a new control technique was introduced using neural networks and fuzzy logic controllers applied to a grid-connected doubly fed induction generator to maximize turbine power output. Based on the simulation findings, the neuron controller significantly reduced the response time while constraining and surpassing peak values compared to the fuzzy logic controller [46]. Moreover, a novel combined maximum power point tracking (MPPT)-pitch angle robust control system of a variable-speed wind turbine using ANNs was reported; the simulations showed a 75% improvement in power generation in comparison to typical controllers [47]. Following this work, others discussed the merge of adaptive neuro-fuzzy controllers and their improved performance on MPPT in WECS [48,49]. Soft computing approaches using optimization were and continue to be explored for MPPT problems. For instance, particle swarm optimization (PSO) tracks the maximum power point without measuring the rotor speed, reducing the controller’s computation needs [50]. As for forecasting, 6400 min of wind measurement arrays were used to achieve a 14% improvement in the accuracy of wind power 12-step ahead forecasting using RNNs [51]. Later, ANNs were used to forecast 120 steps ahead, achieving 27% higher accuracy than the benchmark [52]. Forecasting for WECS and other systems heavily relies on neural networks, as they are robust and accurate. The continuous growth in this field is evident by the consistent links throughout the years of this field. Interest in this cluster continues to grow and be active. However, research on wind power forecasting has become scarce since 2009.
- Wave Energy Conversion Systems (#1). The highest frequency keywords include forecasting (116 sources), wave energy (42 sources) and converters (33 sources), deep neural network (24 sources), and significant wave height (4 sources). Influential data science works in wave energy began catching attention in 2015. Due to the computational complexity associated with analyzing wave energy converter arrays and the escalating computational demands as the number of devices in the system grows, initial significant research efforts focused on finding optimal configurations for these arrays. For instance, a combination of optimization strategies was applied. The suggested methodology involved a statistical emulator to forecast array performance. This was followed by the application of a novel active learning technique that simultaneously explored and concentrated on key areas of interest within the problem space. Finally, a genetic algorithm was employed to determine the optimal configurations for these arrays [53]. These methods were tested on 40 wave energy converters with 800 data points and proved to be extremely fast and easily scalable to arrays of any size [53]. Studies focused on control methods to optimize energy harvesting of a sliding-buoy wave energy converter were reported in [54], using an algorithm based on a learning vector quantitative neural network. Later, the optimization approach became more popular in publications on forecasting for wave energy converters. A multi-input convolutional neural network-based algorithm was applied in [55] to predict power generation using a double-buoy oscillating body device, beating out the conventional supervised artificial network and regression models by a 16% increase in accuracy. It also emphasized the significance of larger datasets, pointing out that increasing the size of the dataset could capture more details from the training images, resulting in improved model-fitting performance [55]. More recent works on forecasting focus on significant wave height prediction using experimental meteorology data and hybrid decomposition and CNNs methods, ultimately achieving 19.0–25.4% higher accuracy [56,57]. Interest in this cluster continues to grow.
- Integrated Renewable Energy System Management (#2). This cluster combines the publications related to the management of integrated renewable energy systems [58], power generation using the organic Rankine cycle [59] and power systems outage outages [60], biomass energy conversion [61,62], and hybrid systems [63] using knowledge-based design tools, mixed-integer linear programming, genetic algorithm, decision support systems, and ANNs. These topics were abundant in the late 1990s and 2005–2015. However, more recent publications on these topics are scarce.
- Solar Power Generation (#3). Containing keywords related to photovoltaics and their systems (29 sources), general solar power (29 sources), and solar–thermal energy conversion (44 sources), this cluster is consistently growing and active. Early works in this cluster focused on power forecasting for photovoltaics since insolation is not constant and meteorological conditions influence output. Ref. [64] reports the choice of the radial basis function neural network (RBFNN) for its structural simplicity and universal approximation property and RNN for it is a good tool for time series data forecasting for 24-hour ahead forecasting with RMSE as low as 0.24. As the solar cells market grew favorably in 2009, publications also explored the feasibility of ANNs for MPPT of crystalline-Si and non-crystalline-Si photovoltaic modules with high accuracy, MSE as low as 0.05 [65,66]. It also explored temperature-based modeling without meteorological sensors using gene expression programming for the first time and other AI models such as ANNs and ANFIS. The ANNs reduced RMSE error by 19.05% compared to the next-best model [67]. Similar studies were conducted using a hybrid ANN with Levenberg–Marquardt algorithm on solar farms with a 99% correlation between predictions and test values [68]. Other than forecasting tasks, publications also range on topics like smart fault-tolerant systems using ANNs for inverters [69] to MPPT of a three-phase grid-connected photovoltaic system using particle swarm optimization-based ANFIS [70]. More recent papers focus on using more attribute-rich data to perform short-term forecasting of solar power generation for a smart energy management system using ANNs [71], gradient boosting machine algorithms [72], and a more complex multi-step CNN stacked LSTM technique [73] with great accuracy. The emergence of complex boosting techniques is interesting because these algorithms tend to learn faster and in a computationally less costly way than NNs but with similar accuracy.
- Deep Learning Algorithms (#5, #6, #8). These clusters were grouped into one because of the popular deep learning algorithms. The artificial neural network (#5) showcases many vertical links; ANNs are popular across almost all energy conversion tasks and domains for their ability to learn and model nonlinear relationships. In [74], ANN algorithms are used to model a diesel engine with waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption, and exhaust emissions, ultimately achieving an MSE as low as 0.0004. An application of an ANN with Levenberg–Marquardt learning algorithm technique for predicting hourly wind speed was reported in [75]. ANNs were popular in the early years of adoption until 2015. Recent studies, where ANNs are the highlighted algorithm, tend to be newer areas of exploration in energy conversion and data sciences. For instance, using 36,100 experimental data points of combustion metrics to control combustion physics-derived models, a 2.9% decrease in MAPE was achieved using ANNs. [76]. However, in most recent studies, ANNs are used as the benchmark of comparison for a more complex or efficient method.

- Electric Power Transmission (#7). This cluster contains research related to the stability and management of unified power flow controllers (UPFC), electrical grids, and transmission lines. In 2008, IEEE held a conference titled “Conversion and Delivery of Electrical Energy in the 21st Century”; several noteworthy publications are included in this cluster. A Lyapunov-based adaptive neural network UPFC was applied to improve power system transient stability [87]. Outage possibilities in the electric power distribution utility are modeled using a para-consistent logic and paraconsistent analysis nodes [60]. Linear matrix inequality optimization algorithms were used to design output feedback power system stabilizers, which ultimately improved efficiency by 68%, compared to the standard controller [88]. A first look at the utility of reinforcement learning was explored by applying a Q-learning method-based on-line self-tuning control methodology to solve the automatic generation control under NERC’s new control performance standards, which achieved all proposed constraints and achieved a 6% reduction in error compared to the traditional controller [89]. After a gap of inactivity in this cluster, a resurgence of these topics began in 2020, focusing heavily on reinforcement learning for smart grid control and more thorough agent, environment, and state definition. A Q-learning agent power system stabilizer is designed for a grid-connected doubly fed induction generator-based wind system to optimize control gains online when wind speed varies, amounting to a nine-time more stable controller [90]. The use of an advanced deep reinforcement learning approach is applied to energy scheduling strategy to optimize multiple targets, including minimizing operational costs and ensuring power supply reliability of an integrated power, heat, and natural-gas system consisting of energy coupling units and wind power generation interconnected via a power grid using a robust 60,000 data points and achieving 21.66% efficiency over particle swarm optimization and outperforming a deep Q-learning agent [91]. As energy systems become increasingly integrated with more complex sourcing and distribution, the interest in this field of publications will continue to grow.
- Battery/Charging Management System (#9). This cluster relates to electric vehicle charging, scheduling, and lithium-ion battery management. In dynamic wireless power transfer systems for electric vehicle charging, the degree of LTM significantly affects energy efficiency and transfer capability. An ANN-based algorithm was propositioned to estimate the LTM value; i.e., the controller would have the ability to establish an adjusted reference value for the primary coil current, offsetting the decrease in energy transfer capacity due to LTM, thus leading to a 32% increase in the value of the transferred energy [92]. A neural network energy management controller (NN-EMC) is designed and applied to a hybrid energy storage system using the multi-source inverter (MSI). The primary objective is to manage the distribution of current between a Li-ion battery and an ultracapacitor by actively manipulating the operational modes of the MSI. Moreover, dynamic programming (DP) was used to optimize the solution to limit battery wear and the input source power loss. This DP-NN-EMC solution was evaluated against the battery-only energy storage system and the hybrid energy storage system MSI with 50% discharge duty cycle. Both the battery RMS current and peak battery current have been found to be reduced by 50% using the NN-EMC compared to the battery-only energy storage system for a large city drive cycle [93]. Battery/ultracapacitor hybrid energy storage systems have been widely studied in electric vehicles to achieve a longer battery life. Ref. [94] presents a hierarchical energy management approach that incorporates sequential quadratic programming and neural networks to optimize a semi-active battery/ultracapacitor hybrid energy storage system. The goal is to reduce both battery wear and electricity expenses. An industrial multi-energy scheduling framework is proposed to optimize the usage of renewable energy and reduce energy costs. The proposed method addresses the management of multi-energy flows in industrial integrated energy systems [95]. This field borrows from a diverse set of algorithms.
- Induction Machine, Digital Storage, and Control System (#10, #12, #13). These three clusters have lost momentum and become unexplored in recent years. Regarding the induction machine, a sensorless vector-control strategy for an induction generator operating in a grid-connected variable speed wind energy conversion system was presented using an RNN, offering a 4.5% improvement upon the benchmark [96]. Cluster #12 combines the publications related to the accurate modeling of state-of-charge and battery storage using ANNs and RNNs [97,98,99]. Cluster #13 includes the publications related to maximum point tracking in wind energy conversion systems using neural network controllers [100,101,102] and energy maximization using neural networks, such as learning vector quantitative neural networks, on a sliding-buoy wave energy converter [54].
- Genetic Algorithm (#11). This cluster deals primarily with the application of the genetic algorithm to various energy conversion systems, as well as other optimization techniques. A genetic algorithm solves both constrained and unconstrained optimization problems, similar to how genetics evolve in nature through natural selection and biological evolution. The genetic algorithm revises a population of individual solutions as it explores different solutions. [103]. Evident on the timeline of this cluster is the consistent use of this optimization technique throughout the years; optimization techniques are often part of a typical data science project pipeline, where the data science algorithm transforms inputs into outputs, and the optimization tools can be used to optimize the inputs, outputs, or algorithm itself. Thus, ANNs or other neural networks are often paired up with the genetic algorithm. A review of how the genetic algorithm is often used to optimize the input space of ANN models and investigate the effects of various factors on fermentative hydrogen production was reported in [104]. A multi-objective genetic algorithm was employed to derive a Pareto optimal collection of solutions for geometrical attributes of airfoil sections designed for 10-meter blades of a horizontal axis wind turbine. The process utilized ANN-modelled objective functions and was discussed in [105]. Ref. [106] reported the high performance and durability of a direct internal reforming solid oxide fuel cell by coupling a deep neural network with a multi-objective genetic algorithm, improving the high-power density by 190% while significantly reducing carbon deposition. A similar approach was undertaken to maximize the exergy efficiency and minimize the total cost of a geothermal desalination system [107].

## 4. Strengths and Weaknesses

#### 4.1. Ranking Publications

#### 4.2. Artificial Neural Networks

**Figure 8.**The general structure of an ANN (from [114]).

#### 4.2.1. Strengths

- nonlinearity allows for a great fit to almost any dataset,
- noise-insensitivity provides accurate prediction in the presence of slight errors in data, which are common,
- high parallelism allows for fast processing and hardware failure-tolerance,
- the system may modify itself in the face of a changing environment and data by training the neural network once again, and
- its ability to generalize enables the application of the model to unlearned and new data.

#### 4.2.2. Weaknesses

- ANNs’ success depends on both the quality and quantity of the data;
- A lack of decisive rules or guidelines for optimal ANN architecture design;
- A prolonged training time, which could extend from hours to months;
- The inability to comprehensibly explain the process through which the ANN made a given output, often criticized for being black boxes;
- There are parameters that require optimizing, which are at times not intuitively apparent [119].

#### 4.3. Ensemble Learning: XGBoost, Random Forest, Support Vector Machine, Decision Trees

#### 4.3.1. Strengths

#### 4.3.2. Weaknesses

#### 4.4. Long Short-Term Memory

_{t−1}, C

_{t−1}, and x

_{t}and two outputs h

_{t}and C

_{t}. For a given time t, h

_{t}is the hidden state, C

_{t}is the cell state or memory, x

_{t}is the current data point or input. The first sigmoid layer inputs are h

_{t−1}and x

_{t}. This component is referred to as the forget gate because its output determines the extent to which information from the previous cell should be retained. The output of the forget gate is a value between 0 and 1, which is then element-wise multiplied with the previous cell state C

_{t−1}. As for the second sigmoid layer, it is the input gate, which chooses what new information makes it to the cell. It takes two inputs h

_{t−1}and x

_{t}, the previous hidden state and the current data point. The tanh layer creates a vector C

_{t}of the new candidate values. These second sigmoid layer and the tanh layer determine the information to be stored in the cell state. Their point-wise multiplication determines the amount of information to be added to the cell state. The outcome of the input gate is combined with the outcome of the forget gate, which is multiplied by the previous cell state, resulting in the generation of the current cell state C

_{t}. Subsequently, the cell’s output is computed by utilizing the third sigmoid layer and the tanh layer. The former determines the portion of C

_{t}that will be incorporated into the output, while the latter adjusts the output within the [−1, 1] range. Finally, these results undergo point-wise multiplication to produce the final output h

_{t}of the cell [134,135,136].

#### 4.4.1. Strengths

#### 4.4.2. Weaknesses

#### 4.5. Convolutional Neural Network

#### 4.5.1. Strengths

#### 4.5.2. Weaknesses

- CNNs’ success also depends on both the quality and quantity of the data;
- A lack of decisive rules or guidelines for optimal CNN architecture design, although different architectures have been studied and show promising results;
- A prolonged training time, which could extend from hours to months;
- The explainability of CNNs outputs is also limited;
- There are parameters and choices in layers that require optimizing, sometimes leading to trial and error until the desired accuracy or error rate is reached.

#### 4.6. Adaptive Network-Based Fuzzy Inference System

#### 4.6.1. Strengths

- Capability to capture the nonlinear structure of a process;
- Strong adaption capability;
- Rapid learning capacity, thanks to its parallel computation capabilities;
- Fewer adjustable parameters than ANNs;
- Universal application.

#### 4.6.2. Weaknesses

#### 4.7. Reinforcement Learning: Q-learning, Deep Deterministic Policy Gradient, Actor Critic

#### 4.7.1. Strengths

#### 4.7.2. Weaknesses

#### 4.8. Other Algorithms Worth Mentioning: Graph Neural Networks and Regression Methods

## 5. Conclusions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

AI | artificial intelligence |

ANN | artificial neural network |

ANFIS | adaptive neuro-fuzzy inference system |

ARIMA | autoregressive integrated moving average |

BPNN | backpropagation neural network |

CNN | convolutional neural network |

DDPG | deep deterministic policy gradient |

GNN | graph neural network |

LSTM | long short-term memory |

LTM | lateral misalignment |

ML | machine learning |

MPPT | maximum power point tracking |

MSE | mean squared error |

NN-EMC | neural network energy management controller |

RAM | random access memory |

RBFNN | radial basis function neural network |

RL | reinforcement learning |

RMS | root mean square |

RMSE | root-mean-square error |

RNN | recurrent neural network |

SAC | soft actor critic |

SVM | support vector machine |

UPFC | unified power flow controller |

WECS | wind energy conversion systems |

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**Figure 2.**Bibliometric map of the interconnection of keywords associated with energy conversion and management and ML/AI (VOSViewer software).

**Figure 3.**Bibliometric (

**a**) and year-based (

**b**) maps of the interconnection of unique keywords related to energy conversion and ML/AI in documents that include real data, data size, performance measure, and a comparison benchmark (VOSViewer software).

**Figure 6.**Comparison between simulated and real data: (

**a**) data size; (

**b**) benchmarked performance measure.

Programming Languages or Software | Frequency | Years |
---|---|---|

MATLAB | 157 | 1999–2022 |

Python | 35 | 2017–2022 |

SIMULINK | 20 | 2012–2021 |

Mathematica | 1 | 2020 |

Java | 1 | 2020 |

R | 1 | 2017 |

C++ | 1 | 2011 |

Not mentioned | 387 | - |

Criteria | Weight | Rankine | |||||
---|---|---|---|---|---|---|---|

0 | 1 | 2 | 3 | 4 | 5 | ||

Data size (number of samples) | 25% | 0–1000 | 1000–9000 | 9000–12,500 | 12,500–16,000 | 16,000–20,000 | +20,000 |

Data information | 25% | Not mentioned | – | Simulation data | – | – | Real data |

Performance achieved by an algorithm | 25% | Not mentioned | Objective achieved | 0–10% | 10–30% | 30–100% | +100% |

Year of publication | 10% | …–1999 | 2000–2010 | 2011–2013 | 2014–2016 | 2017–2019 | 2020–2022 |

Number of citations (status end 2022) | 10% | 0–5 | 5–10 | 10–15 | 15–50 | 50–100 | +100 |

Algorithm type | 2.5% | – | Deep learning and graph-based | – | Reinforcement learning | Supervised machine learning and optimization | Supervised machine learning and optimization |

Memory requirement (GB of RAM) | 2.5% | – | 64, 50, 48, or greater | – | 16 | 8 | 4 or less |

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## Share and Cite

**MDPI and ACS Style**

Mira, K.; Bugiotti, F.; Morosuk, T.
Artificial Intelligence and Machine Learning in Energy Conversion and Management. *Energies* **2023**, *16*, 7773.
https://doi.org/10.3390/en16237773

**AMA Style**

Mira K, Bugiotti F, Morosuk T.
Artificial Intelligence and Machine Learning in Energy Conversion and Management. *Energies*. 2023; 16(23):7773.
https://doi.org/10.3390/en16237773

**Chicago/Turabian Style**

Mira, Konstantinos, Francesca Bugiotti, and Tatiana Morosuk.
2023. "Artificial Intelligence and Machine Learning in Energy Conversion and Management" *Energies* 16, no. 23: 7773.
https://doi.org/10.3390/en16237773